tbuckley/cabot-search huggingface dataset:
A phase II basket trial of dual anti–CTLA-4 and anti–PD-1 blockade in rare tumors (DART) SWOG S1609: The vulvar cancers.
Authors: Young Kwang Chae, Megan Othus, Sandip Pravin Patel, Robert P. Edwards, Jennifer Michelle Scalici, Liam Il-Young Chung, Christine McLeod, Helen X. Chen, Elad Sharon, Howard Streicher, Christopher W. Ryan, Charles David Blanke, Razelle Kurzrock
Journal: Journal of Clinical Oncology (2023)

5517 Background: Dual checkpoint inhibition with Anti-PD-1 and anti-CTLA4 checkpoint inhibitors have proven to be effective in several malignancies but their potential role in various rare solid cancers is yet to be established. The efficacy of immunotherapy in vulvar cancer patients has not been explored. This study presents the first results of ipilimumab and nivolumab in vulvar cancers (cohort 35) of the SWOG S1609 Dual Anti-CTLA-4 &amp; Anti-PD-1 blockade in Rare Tumors (DART) trial. Methods: DART is a prospective, open-label, multicenter/multi-cohort phase 2 clinical trial of ipilimumab (1mg/kg intravenously every 6 weeks) plus nivolumab (240mg intravenously every 2 weeks). The primary endpoint was objective response rate (ORR) (RECIST v1.1) (confirmed complete (CR) and partial responses (PR)); progression-free survival (PFS), overall survival (OS), stable disease (SD) &gt; 6 months, and toxicity are secondary endpoints. Results: Sixteen evaluable patients (median age, 55.5 years) were analyzed. 14 cases were of squamous cell carcinoma histology and 2 were of poorly differentiated carcinoma. ORR was 18.8% (3/16, 25% when including one unconfirmed PR). There was 1 CR (SCC; PFS of 465 days) and 2 PR (both SCC; one with 57% regression with PFS of 1022 days, another with 53% regression with PFS of 501 days). Of note, there was one SCC patient with unconfirmed PR that showed 69% regression with PFS of 209 days. Overall clinical benefit rate (CBR; no progression &gt; 6months) was 31.3% (5/16). The median PFS was 2.2 months, 6-month PFS 33%, 1-year PFS 20%. The median OS was 7.6 months, 6-month OS 62%, 1-year OS 44%. The most common adverse events were diarrhea, fatigue, pruritus, anorexia, and nausea (25%, n = 4 each). Grade 3-4 adverse events occurred in 25% of patients (n = 4). There was 1 grade 3-4 adverse event (6.7%) that led to discontinuation, and 1 (6.7%) grade 5 death adverse event. Conclusions: Ipilimumab plus nivolumab in vulvar cancers resulted in an ORR of 18.8% and CBR of 31.3%, with durable responses seen. Correlative studies to determine response and resistance markers are ongoing. Expanded prospective studies exploring the role of immunotherapy in vulvar cancers are warranted. Clinical trial information: NCT02834013 .
Preliminary results of a phase II trial with sacituzumab govitecan-hziy in patients with recurrent endometrial carcinoma overexpressing Trop-2.
Authors: Alessandro Santin, Blair McNamara, Eric R. Siegel, Justin Harold, Levent Mutlu, Gary Altwerger, Gloria S. Huang, Vaagn Andikyan, Mitchell B. Clark, Elena Ratner, Masoud Azodi, Peter Dottino, Peter E. Schwartz, Stefania Bellone
Journal: Journal of Clinical Oncology (2023)

5599 Background: Despite recent advances, there remains a significant need for targeted, efficacious treatments for advanced and recurrent endometrial carcinoma (EC). Trop-2 has been demonstrated to be overexpressed in several EC including grade 3 endometrioid adenocarcinoma (96%), and uterine serous carcinoma (65%). Trop-2 overexpression has been found to confer a worse prognosis and predicts disease recurrence. Sacituzumab govitecan-hziy (SG) is an antibody-drug conjugate comprised of a humanized anti-Trop-2 antibody conjugated with the active metabolite of irinotecan, a topoisomerase-I inhibitor. SG is currently FDA-approved for use in metastatic urothelial cancer and metastatic triple-negative breast cancer. Methods: We report results from the stage 1 of an open-label non-randomized phase 2 trial (NCT04251416) established to evaluate the clinical activity of SG in patients with persistent or recurrent EC of epithelial origin with Trop-2 overexpression. Criteria for study participation included radiographically confirmed and measurable persistent or recurrent EC, progression after or during platinum-based chemotherapy, and at least 2+ staining of Trop-2. One target lesion by RECIST v1.1 and adequate bone marrow, renal, and hepatic function were required. Patients were at least two weeks beyond prior treatment, major surgery, or high dose systemic corticosteroid use. Patients were given 10mg/kg of SG administered as an intravenous infusion once weekly on days 1 and 8 of 21-day treatment cycles. This 3-week cycle was continued without rest until disease progression or unacceptable toxicity. Results: Twenty-one patients were enrolled into stage 1. Ten patients (48%) had uterine serous carcinoma, seven (33%) had endometrioid adenocarcinoma, three (14%) had carcinosarcoma, and one patient had mixed serous and clear cell histology. All patients received at least one prior line of chemotherapy, (median 3 lines, range 1-6). At the time of submission, 20 patients were response-evaluable (Table); seven (35%) achieved objective response (5 out of 7 confirmed). Eighteen patients were evaluable for durable disease control; 7 (39%) achieved it. Follow-up durations had a median (IQR) of 15.6 (5.7–22.5) months. Median OS was 22.5 months, and median PFS was 5.7 months. The treatment was well-tolerated with no new or unexpected safety signals reported. Conclusions: Sacituzumab govitecan shows remarkable clinical activity against some of the most historically challenging and chemotherapy-resistant endometrial pathologies and demonstrates a favorable safety profile. With these promising results, we will begin the stage 2 expansion of this phase 2 trial. Clinical trial information: NCT04251416 . [Table: see text]
A real-world study of US patients with metastatic ovarian, fallopian tube, and peritoneal cancer (mOFPC) using integrated electronic health records (EHR) and claims datasets.
Authors: Avinash P B, Gabriel Funingana, Neeraj Singh, Rohini George, Anna Sowa, Sangavai Chakkrapani, Vandana Priya, Vivek Prabhakar Vaidya, Laura Vidal, Kamal S. Saini, Smita Agrawal
Journal: Journal of Clinical Oncology (2023)

e17548 Background: Ovarian cancer is the most lethal gynecologic cancer. Detailed epidemiologic descriptions from a real-world database of a large population of ovarian and related cancer patients can provide insights into the characteristics, treatment patterns, and outcomes of these patients, and could serve as a useful benchmark while developing new therapies. Methods: This retrospective analysis uses the ConcertAI RWD360 dataset which is a real-world de-identified dataset of more than 6 million patients from across the United States derived from various EHR systems and integrated with a large administrative open claims dataset (&gt;90% overlap). 60429 patients in this dataset had OFPC out which 12406 patients had metastatic cancer. We present here the key characteristics of this mOFPC cohort including histology, biomarkers and top treatment patterns. We also performed overall survival (OS), time to treatment discontinuation (TTD) and time to next treatment (TTNT) analyses starting from initiation of 1st line of therapy (LoT) for all patients who received systemic treatment after metastatic diagnosis (8308 patients). Results: The top 5 histologies and biomarkers tested in this mOFPC cohort are presented in Table. The median OS from start of 1st LoT for this cohort was 2.45 years. The median TTD and TTNT across all 1st line therapies were 2.5 months and 5.5 months respectively. Top 2 treatments given as 1st LoT after metastatic diagnosis are also presented in Table. Chemotherapy (cis/carboplatin + pacli/docetaxel) and chemotherapy + bevacizumab were the most common 1st LoTs and there was no difference in the OS of patients treated with these two LoTs. Conclusions: This study describes the characteristics of a large US real-world cohort of mOFPC patients. Here we have presented data on the overall cohort, but further analysis of outcomes stratified by treatment arms, biomarkers and histology can be performed to better inform best treatments for specific sub-cohorts. [Table: see text]
Homologous recombination-related (HRR) gene mutation profiling and the association with TMB in Chinese patients with pancreatic cancer.
Authors: Yin Bing Wu, Huan Yi, Yangyang Yu, Mingzhe Xiao, Chuang Qi
Journal: Journal of Clinical Oncology (2023)

e16252 Background: Homologous recombination deficiency (HRD) is associated with tumorigenesis and could predict the response of PARP inhibitor (PARPi) therapy and immunotherapy. Loss of function or deleterious mutations in homologous recombination-related (HRR) genes contribute to HRD phenotype. Methods: Tumor tissue samples from 669 Chinese patients with pancreatic cancer were sequenced with 551 cancer-related genes. Based on the clinical evidence, 15 HRR genes ( ATM, BRCA1, BRCA2, BARD1, BRIP1, CDK12, CHEK1, CHEK2, FANCL, PALB2, RAD51B, RAD51C, RAD51D, RAD54L, PPP2R2A) were selected. 495 patients with WES data from the TCGA datasets were included for analysis. Results: In the Chinese cohort, 10.46% (70/669) patients exhibited somatic HRR (sHRR) gene mutations. ATM (4%) was the most common mutant gene, followed by BRCA2 (3%), BARD1 (1%), BRCA1 (1%), BRIP1 (1%), RAD51D (1%), CHEK2(1%). The germline HRR (gHRR) gene mutations were identified in 55.67% (118/245) patients. BRCA2 (23%) was the most common mutant gene, followed by ATM (10%), BRCA1 (9%), BRIP1 (9%), PALB2 (8%), BARD1 (7%), FANCL (3%). 1.49% (10/669) patients carried both germline and somatic HRR gene mutations. The sHRR-Mut patients had a higher median TMB compared to the sHRR-WT patients (3.97 vs 2.13, p&lt;0.001). However, the median TMB was similar between the gHRR-Mut and the gHRR-WT patients (2.13 vs 2.13, p=0.49). In the TCGA cohort, 9.09% (45/495) patients exhibited sHRR gene mutations. The most common mutant gene was ATM (4%), followed by CDK12 (2%) BRCA2 (2%), BARD1 (1%). The sHRR-Mut patients had a higher median TMB compared to the sHRR-WT patients (1.03 vs 0.71, p&lt;0.001). Totally, the somatic mutant frequency of HRR genes was similar between Chinese cohort and TCGA cohort (10.46% vs 9.09%, p = 0.113). For the TCGA cohort, the patients with sHRR-Mut had a similar median progression free survival compared to the ones with sHRR-WT (74.4 months vs not reached, p = 0.299). Conclusions: Our data reported the genetic landscape of HRR gene in Chinese pancreatic cancer patients. Patients with somatic HRR gene mutations had a significantly elevated TMB level. Our data suggest that patients with altered HRR genes may be rational candidates for precision oncology treatment and provide new opportunities to predict the tumor response to multiple treatments.
Auranofin and the efficacy of anti-PD1 immunotherapy.
Authors: Yan Zhang
Journal: Journal of Clinical Oncology (2023)

e21180 Background: Anti-PD-1 immunotherapy brings hope for the treatment of malignant tumors, but its overall response rate is low.Therefore, how to sensitize anti-PD-1 therapy to make more patients benefit from immunotherapy is an urgent clinical demand. Methods: PKCι, as a classical oncogenic gene that affects cell proliferation and differentiation, has recently been found to participate in the formation of tumor immunosuppressive microenvironment (TIME). TIME directly leads to resistance to anti-PD1 therapy, so by inhibiting the expression of PKCι to regulate the inhibitory tumor immune microenvironment and sensitize anti-PD1 therapy. Auranofin is a commonly used anti-inflammatory drug in clinic. Recently, it was found that it can inhibit the expression of PKCι and retard growth of tumor. Results: Our study found that interference with PKCι expression in tumor cells can reverse the immunosuppressive microenvironment and enhance the effect of anti-PD1 therapy. Auranofin (PKCι inhibitor) combined with anti-PD-1 immunotherapy has better anti-tumor effect than single drug therapy. Auranofin can inhibit the expression of PKCι and its downstream YAP1 and TNFα, reduce the recruitment of immunosuppressive cells such as MDSCs and Treg, promote the infiltration of effector T cells, ultimately reverse the inhibitory immune microenvironment and play a synergistic anti-tumor role. Conclusions: In summary, the combination of Auranofin and anti-PD-1 immunotherapy can reverse the TIME to sensitize the immune effect, play a synergistic anti-tumor effect, and provide a new idea and theoretical basis for immunotherapy of malignant tumors.
The accuracy of the Psychological Distress Inventory – Revised (PDI-R) in identifying distress in oncological outpatients: A pilot study using a machine learning approach.
Authors: Alessandro Alberto Rossi, Maria Marconi, Stefania Mannarini, Luigi De Cicco, Claudio Verusio
Journal: Journal of Clinical Oncology (2023)

e24222 Background: Ever greater importance is given to psychological distress in oncological settings. Distress could have a severe adverse effect on adherence and compliance to therapies, medical treatments, and quality of life (NCCN, 2015). Moreover, psychological research is progressively increasing in oncological settings and psychological distress has rapidly gained popularity – leading to the development of scales aimed at its evaluation. In this regard, the revised version of the Psychological Distress Inventory – the PDI-R (Rossi et al., 2022) – has demonstrated excellent psychometric properties but its screening properties were limited to self-report questionnaires. Consequently, to fill this gap, this study aimed to evaluate the accuracy of the PDI-R in identifying the levels of distress of oncological outpatients compared to a diagnostic interview – using a machine learning approach. Methods: Oncological outpatients ( n = 603; mean age = 68.38, SD = 9.31; 316 males) were enrolled at the Presidio Ospedaliero of Saronno, ASST Valle Olona, Italy. Patients were tested with the PDI-R which is composed of 8 items that evaluate internal, external, and general distress. In this study, the PDI-R shows good internal consistency: McDonald’s omega = .922. Then, patients were interviewed by a psycho-oncologist who assessed distress levels of each patient. In accordance with the methodological procedures for the application of machine learning algorithms, patients were randomly divided into two different databases. The first database ( n = 400) was used to train the machine learning algorithm and the second one ( n = 203) was used to test the accuracy of its predictions. The k-nearest neighbors ( k-NN) algorithm was applied. Results: According to the clinical interview, the training database (mean age = 67.78, SD = 9.10; 212 males) shows that 76% of patients were without distress (or mild distress) and 24% had moderate or severe distress. Moreover, according to the clinical interview, the testing database (mean age = 69.58, SD = 9.62; 104 males) shows that 71% of patients were without distress (or mild distress) and 29% had moderate or severe distress. Using the 8 items of the PDI-R, the k-NN algorithm, correctly identified 193 subjects (144 true negatives and 49 true positives). At the same time, only 10 subjects (10 false negatives) were incorrectly classified – thereby showing a machine learning-based accuracy of 95%. Conclusions: These findings suggest that the PDI-R is a reliable self-report questionnaire with strong psychometric properties and high accuracy in identifying psychological distress. Its use in research and clinical practice is therefore recommended to increase the quality of both assessment and treatment of psychological distress in patients with oncological difficulties.
County-level jail incarceration and cancer mortality in the US.
Authors: Jingxuan Zhao, Sandhya Kajeepeta, Xuesong Han, Leticia M Nogueira, Zhiyuan Zheng, Qinjin Fan, Kewei Sylvia Shi, Robin Yabroff
Journal: Journal of Clinical Oncology (2023)

6535 Background: Incarceration has been linked to poor community health outcomes, such as worse preterm births and mortality in the US, although little research has examined cancer outcomes. This study examined associations of county-level jail incarceration and cancer mortality. Methods: Annual county-level local jail incarceration rates (1990-2018) were obtained from the Vera Institute of Justice. We calculated annual county-level mortality rates (2000 -2019) with invasive cancer as the underlying cause of death (ICD-10 codes: C00-C97) using National Vital Statistics System. Associations of county-level jail incarceration and cancer mortality overall, and by sex, race, and common cancer sites were examined with generalized estimating equations with Poisson distribution and standard errors clustered at county level. To assess the short-, medium-, and long-term associations, we used lagged county-level incarceration rates by 1, 5, and 10 years prior to mortality rates in separate analyses. Results: Over the 20-year study period, each 1 per 1000 increase in county jail incarceration rate was associated with a 1.2% increase in cancer mortality rate in the short-term (model with 1-year lags, rate ratio (RR): 1.012, 95% CI: 1.009–1.015) and 1.1% increase in medium-term (5-year lags (RR: 1.011, 95% CI: 1.008–1.014) and 0.8% increase long-term (10-year lags (RR: 1.008, 95% CI: 1.004–1.012). After adjusting for county-level sociodemographic characteristics, the RRs were attenuated but remained statistically significant. Jail incarceration was associated with higher cancer mortality for all common cancers included in the study (Table) and for White people, but not for Black people. The magnitude of associations was similar for medium- and long-term effects (5- and 10-year lags, respectively). Conclusions: Higher county-level jail incarceration rates were associated with higher county-level cancer mortality rates, underscoring the collateral health consequences of mass incarceration. Efforts to identify interventions to decrease the cancer mortality burden in these communities are warranted. [Table: see text]
Key difference-making conditions distinguishing ASCO members’ institutions that collect sexual orientation and gender identity (SOGI) data from those that do not.
Authors: Mandi L. Pratt-Chapman, Megan A Mullins, Edward J Miech, Janette K. Merrill, Alex Swierz, Kimberly T Smith, Shine Chang, Gwendolyn P. Quinn, Matthew B. Schabath, Shail Maingi, Joseph Astorino, Stephen C Meersman, Charles Stewart Kamen
Journal: Journal of Clinical Oncology (2023)

e18554 Background: Despite ASCO and NIH calling it a priority, sexual orientation and gender identity (SOGI) data are not routinely collected in most cancer care settings. The aim of this analysis was to identify the key difference-making conditions that distinguish ASCO members’ institutions that collect SOGI data from those that do not. Methods: In fall of 2020, an anonymous 54-item electronic survey was distributed to ASCO members in oncology practice or research, presumably from non-overlapping institutions. We used configurational analysis to identify difference-making configurations for the presence and absence of institutional SOGI data collection. This approach applies Boolean logic and set theory, and like machine learning, is both novel and agnostic. Results: Three factors consistently distinguished sites (n = 45) that reported collecting both SO and GI data (n = 25) from those that did not (n = 20). The positive model consisted of two solution pathways: (1) replying “always” or “most of the time” to the question “Do you ask your patients what pronouns they want you to use for them?” or (2) replying “strongly agree” or “agree” to the statement “Institutional leadership supports collecting sexual orientation and gender identity (SOGI) data from patients” and replying “yes, the health record has a specific page/tab/section for SOGI data” to the question “Does the electronic health record at your institution have a specific section to collect information about patients' SOGI?” (consistency = .87, coverage = .80). The negative model consisted of three solution pathways: (3) replying “strongly disagree” or “disagree” that leadership supports collecting SOGI data OR (4) replying “no, the health record does not allow for collection of SOGI data” or “not sure” to whether the EHR supports SOGI data collection OR (5) replying “no, the health record does not have a specific section, but it can be recorded as unstructured data in notes” to whether their EHR supports SOGI data collection AND replying “sometimes” or “occasionally” to how often they ask patients about pronoun preferences (consistency = .94, coverage = .80). Conclusions: Leadership support, electronic health record structure, and clinicians asking patients about pronouns consistently distinguished institutions that collected SOGI data from those that did not in this sample. Additional data should be collected to confirm the results, given the small sample size in this study.
Electronic health record (EHR)-based machine learning (ML) to predict disease recurrence after surgical resection of early-stage non-small cell lung cancer (eNSCLC).
Authors: Steven Pisano, Hossein Honarvar, Ravi Bharat Parikh, Rahul K Das
Journal: Journal of Clinical Oncology (2023)

6626 Background: Surgical resection (SR) is a guideline-recommended definitive therapy for patients (pts) with eNSCLC. However, 30–50% pts develop disease recurrence (DR) within the first 5 years (yrs) after surgery. ML applied to routinely collected EHR data could facilitate timely identification of pts at risk of DR who would benefit from enhanced surveillance or initial treatment (Tx) intensification. Methods: eNSCLC pts with stage IB-IIIA at the time of SR between Jan 2010 – February 2022 were identified from ConcertAI Patient360 oncology database comprising structured and curated records from US-based EHR. DR was defined as the human-curated earliest advanced/metastatic diagnosis date from SR. Gradient boosting (GB), random forest (RF), and logistic regression (LR) algorithms with 6-fold nested cross-validation were trained and compared to predict the risk of DR or death at 2 yrs from SR. Pts who did not experience DR and were lost to follow-up within 2 yrs from SR were removed. Feature importance was defined using Shapley Additive Explanation (SHAP)-derived odds ratios (OR). Due to low prevalence of DR at 2 yrs, AUPRC was used as performance metric. Pts in the 1 st and 4 th quartiles of predicted probabilities from XGB model were defined as low-risk vs. high-risk groups. Results: Among 3597 pts, median age was 68.3 yrs (IQR 12.5), 51.8% were female, and 8.6% were Black. 24.4% developed DR within 2 yrs. GB, RF, and LR models had similar ability to predict DR at 2 yrs with mean hold-out set AUPRC of 0.33. In GB model, DR prevalence at 2 yrs was 34% in the high-risk vs. 18% in the low-risk group in hold-out set. N0 stage, T stage &lt; 3, receipt of adjuvant immune checkpoint inhibitor (ICI), and presence of EGFR mutations were protective against DR at 2 yrs, while history of anemia and congestive heart failure (CHF) were risk factors (Table). SHAP revealed a N stage-by-adjuvant therapy and a N stage-by-CHF interaction: benefit from adjuvant chemotherapy (CT) was limited to higher N stages, while adjuvant ICI was beneficial for all N stages. CHF was a risk factor for lower N stages but not for higher N stages. Conclusions: ML applied to structured and unstructuredEHRs identified predictors of risk of DR at 2 yrs after surgery in eNSCLC and may identify high-risk pts who would benefit from enhanced surveillance plans and Tx intensification in the eNSCLC setting. The model indicated favorable outcome from adjuvant targeted therapies in EGFR mutated pts. [Table: see text]